Research Progress and Prospects on Defect Detection and Adaptive Adjustment of Process Parameters in SLM 3D Printing Based on Machine Vision

Yifan Chen

doi:10.47297/taposatWSP2633-456909.20260705

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Research Progress and Prospects on Defect Detection and Adaptive Adjustment of Process Parameters in SLM 3D Printing Based on Machine Vision

Volume 7, Issue 5

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Research Progress and Prospects on Defect Detection and Adaptive Adjustment of Process Parameters in SLM 3D Printing Based on Machine Vision
Research Progress and Prospects on Defect Detection and Adaptive Adjustment of Process Parameters in SLM 3D Printing Based on Machine Vision
School of Mechanical Engineering, Tianjin University of Technology, Tianjin, 300384, China
Vol. 7, Issue 5, Pages: 42-47(2026)
Online First：15 July 2026，

Published：30 May 2026
DOI：10.47297/taposatWSP2633-456909.20260705
Accepted：

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Chen Yifan.Research Progress and Prospects on Defect Detection and Adaptive Adjustment of Process Parameters in SLM 3D Printing Based on Machine Vision[J].Theory and Practice of Science and Technology,2026,07(05):42-47.
DOI：

Chen Yifan.Research Progress and Prospects on Defect Detection and Adaptive Adjustment of Process Parameters in SLM 3D Printing Based on Machine Vision[J].Theory and Practice of Science and Technology,2026,07(05):42-47. DOI： 10.47297/taposatWSP2633-456909.20260705.

摘要

Abstract

Selective Laser Melting (SLM)

as a core technology in additive manufacturing

demonstrates significant application potential in aerospace

biomedical

and high-end equipment manufacturing sectors. However

the complex physical evolution during the SLM process is prone to introducing defects such as spheroidization

cracks

and pores

severely compromising the mechanical performance and reliability of fabricated components. Machine vision technology

with its non-contact

high-precision

and real-time advantages

has become a critical tool for SLM process monitoring and quality control. This paper systematically reviews the design of machine vision-based SLM process imaging systems and image preprocessing techniques

examines the evolution of research on typical defect identification and algorithm optimization

and focuses on exploring implementation pathways for adaptive process parameter adjustment and the feasibility of real-time feedback control. By identifying limitations in existing studies regarding dataset standardization and real-time closed-loop control

the paper outlines future directions for intelligent additive manufacturing.

关键词

Keywords

references

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